SEMANTIC ROLE LABELING FOR AFAAN OROMO SIMPLE SENTENCE

Abstract

Natural language is used to recode human knowledge. Data is stored in computers or on paper in order to be processed and recorded for future use. Semantic role labeling (SRL) is one of the essential problems in the field of natural language processing (NLP) and its task was to determine the semantic roles (such as AGENT and PATIENT) of each argument that corresponds to each predicate in a phrase automatically. SRL is useful shallow semantic representations, and it is a very important intermediate step for several NLP applications, such as Information Extraction, Question Answering and Machine Translation. Traditional methods for SRL are grounded on parsing output, and require much feature engineering. So, the goal of this study was to develop a semantic role labeller for Afaan Oromo text using a deep learning algorithm. To solve Afaan Oromo SRL problems, we employ deep neural networks with long-short term memory (LSTM) and bidirectional long-short term memory (BLSTM). For this study 1800, Afaan Oromo simple sentences were used to train the system, which wassemantically annotated with semantic role label using the BIO Tagging procedure and the PropBank annotation framework's principles. The experiment conducted by using 90%, and 10% of the total dataset for training and testing respectively. Experimental results show that the BiLSTM performed better and achieving the better results in terms of accuracy (80%), precision (81%), recall (80%), and f-measure (80%) as compared to LSTM in terms of accuracy (76%), precision (78%), recall (76%), and f-measure (76%). Based on experimental analysis, concluding remarks and recommendations are forwarded